from PIL import Image
import pytesseract
import argparse
import cv2
import os
 

def tess(img,process,type):

    # load the example image and convert it to grayscale
    image = cv2.imread(img)
    gray = cv2.cvtColor(image, cv2.COLOR_BGR2GRAY)
     
    # check to see if we should apply thresholding to preprocess the
    # image
    if process == "thresh":
        gray = cv2.threshold(gray, 0, 255,
            cv2.THRESH_BINARY | cv2.THRESH_OTSU)[1]
     
    # make a check to see if median blurring should be done to remove
    # noise
    elif process == "blur":
        gray = cv2.medianBlur(gray, 3)
     
    # write the grayscale image to disk as a temporary file so we can
    # apply OCR to it
    filename = "{}.png".format(os.getpid())
    cv2.imwrite(filename, gray)
    text = pytesseract.image_to_string(Image.open(filename),lang=type)
    os.remove(filename)
    return text
     
    # show the output images
    #cv2.imshow("Image", image)
    #cv2.imshow("Output", gray)
    #cv2.waitKey(0)

